What Are the Economic and Labour Market Effects of an Income Tax Reduction Targeted at Older Workers?
Why this work is in the frame
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Bibliographic record
Abstract
This paper explores the economic and labour market effects of implementing a tax reduction targeted at older workers. The analysis is conducted with a life-cycle computable general equilibrium model calibrated on Canadian data. The analysis shows that implementing a permanent income tax reduction for workers aged 60 and over has only small macroeconomic effects because the labour supply increase of older workers is partly offset by a reduction in the labour supply at core ages. This induced effect also discourages savings and generates crowding out through private investment but has a favourable impact on lifetime economic welfare. The macroeconomic impact is much larger when the income tax reduction is temporary because workers no longer reduce their hours at core ages and there is no reduction in savings. However, since only current middle-aged and older workers benefit from the tax cut, a temporary income tax cut reduces intergenerational equity.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it